Learn how you can benefit from our fraud detection solutions.

There has been a major shift in conducting business online – from banking to shopping using mobile devices and computers are the new proxies for customers.

In today’s highly competitive market, financial institutions and businesses must walk a fine line between streamlining customer experiences and preventing fraud. Customers expect interactions to be fast, easy and available on every device – slow them down and they’ll go elsewhere. But fail to properly authenticate them, and fraudulent activity skyrockets.

Neustar can help businesses answer the question – who’s at the end of the connection. We can do that by linking traditional off-line identity verification data (name, address, phone, email, etc.) with online, digital identity data (IP address, geo-location, cookie, device ID, mobile network operator data, and more) to generate a complete view of new or returning customers and their devices. Neustar analyzes these connections to deliver level-of-trust data based on individual and collective connection analyses.

Can the connections between the customer’s identity and the device being used be trusted?

When businesses and financial institutions can gain a clear understanding of who the real customers are behind every interaction, levels of trust rise. When trust is established new customers can create new accounts faster, existing customers can make changes to existing accounts and card not present purchases can all happen without additional hassles, like pushing multifactor authentication or asking knowledge based questions.

Neustar’s trust analysis and insights can help you eliminate customer friction and lower your fraud risk.

IP Intelligence and Reputation
See the location for every IP address and a clear risk score for every IP address behind web transactions. Detect online fraud early in the process—affordably and accurately, without losing good business.

Fraud Risk Models
Use data mining tools to search millions of transactions to spot patterns and detect fraudulent transactions based on learned relationships among known variables.